Autoencoders (AEs) have gained widespread application in the field of hyperspectral anomaly detection, largely due to their notable effectiveness in efficiently reconstructing backgrounds within hyperspectral images (HSIs). However, the absence of prior knowledge and constraints imposed by spectral information capacity hinder the accuracy of anomaly detection by allowing AEs to reconstruct both anomalous targets and backgrounds simultaneously. To address this limitation, a spatial–spectral cross-guided masked autoencoder (SSCMAE) has been proposed. The guided mask is generated based on the spectral difference between the anomaly and the background. This mask effectively suppresses the reconstruction of anomalous targets while enhancing the accuracy of background reconstruction. Moreover, a dual-branch structure operates, encompassing spatial and spectral dimensions, effectively capturing the inherent three-dimensional characteristics present in HSIs. Ingeniously designed cross-connection layers within the architecture enhance the spatial and spectral branches' capability of extracting internal spatial and spectral features of images. In order to capture a more comprehensive range of background features, a lightweight three-dimensional convolutional autoencoder is introduced. This addresses the issue of local feature loss during background reconstruction and overcomes the limitations that visual transformers face when learning local image structures. The proposed method has been systematically compared against several advanced methods on six real-world datasets. The results explicitly demonstrate the efficacy and superior performance of the presented SSCMAE approach.

As the second largest city in northern China, Tianjin has a unique geographical and social status. Following its rapid economic development, Tianjin is experiencing high levels of surface water pollution. The land use/land cover (LULC) pattern has a considerable impact on hydrological cycling and pollutant transmission, and thus on regional water quality. A full understanding of the water quality response to the LULC pattern is critical for water resource management and improvement of the natural environment in Tianjin. In this study, surface water monitoring station data and LULC data from 2021 to 2022 were used to investigate the surface water quality in Tianjin. A cluster analysis was conducted to compare water quality among monitoring stations, a factor analysis was conducted to identify potential pollution sources, and an entropy weight calculation was used to analyze the impact of the land use pattern on water quality. The mean total nitrogen (TN) concentration exceeded the class Ⅴ water quality standard throughout the year, and the correlation coefficient of the relationship between dissolved oxygen (DO) and pH exceeded 0.5 throughout the year, with other water quality parameters showing seasonal changes. On the basis of their good water quality, the water quality monitoring stations near large water source areas were distinguished from those near areas with other LULC patterns via the cluster analysis. The factor analysis results indicated that the surface water in Tianjin suffered from nutrient and organic pollution, with high loadings of ammonia nitrogen (NH₃N), TN, and total phosphorus (TP). Water pollution was more serious in areas near built-up land, especially in the central urban area. The entropy weight calculation results revealed that water, built-up land, and cultivated/built-up land had the greatest impact on NH₃N, while cultivated land had the greatest impact on electrical conductivity (EC). This study discusses the seasonal changes of surface water and impact of land use/land cover pattern on water quality at a macro scale, and highlighted the need to improve surface water quality in Tianjin. The results provide guidance for the sustainable utilization and management of local water resources.

Improving the spatial resolution of hyperspectral (HS) images is of great significance for the subsequent applications. As the multispectral (MS) image can provide abundant complementary land-cover spatial information, HS and MS image fusion (HMF) methods have become a mainstream to generate HS images with both high spatial and spectral resolution. HMF has witnessed rapid progress by leveraging dictionary learning (DicL) technique. However, existing approaches are highly sensitive to the image registration accuracy, and the reconstruction performance of the nonoverlapped spectral bands between HS and MS images is extremely limited. To alleviate the effect of image misregistration and enrich the spectral information of nonoverlapped bands, a general HMF DicL framework, which considers nonoverlapped spectral bands reconstruction and image misregistration, is proposed in this article. For registration error, the proposed method is rectified by the improved DicL, which can solve the problem of the spectral information matching gap existing in traditional HMF methods between HS image and MS image. Meanwhile, for nonoverlapped spectral bands reconstruction, a novel coefficient optimization strategy is adopted to improve the nonoverlapped bands reconstruction. Therefore, the registration error can be avoided to the greatest extent and the accuracy of nonoverlapped bands reconstruction can be effectively improved. Experiments both on simulated and real-world datasets demonstrate that the proposed method can effectively tackle the registration error problem and increase the HMF accuracy with different spectral ranges. Meanwhile, the proposed framework provides guidance significance for the DicL-based HMF methods with various constraints to improve the nonoverlapped bands reconstruction accuracy.

Water is one of a fundamental natural resource and strategic compound. However, with socio-economic development, the water environment is being confronted with a series of problems such as aggravated pollution. Accurate monitoring of the water quality is the prerequisite and foundation for water environment supervision and governance. With the development of remote sensing and computer technologies, the retrieval of water quality parameters has attracted increasing attention from researchers and practitioners. This paper aims to present progress of remote sensing technique in water quality parameter retrieval methods and applications. The following aspects were investigated in this review: (a) water quality parameters retrieval data source; (b) water quality parameters retrieval models and evaluation metrics; (c) water quality parameters remote sensing retrieval applications; (d) some challenges and potential directions for water quality parameters retrieval. This review provides some support for re-searchers, as well as management departments, in theoretical research and application for remote sensing water quality parameters retrieval.

Hyperspectral- multispectral image fusion (HMF) aims to recover a high-resolution hyperspectral image (HrHSI) from an observed high-resolution multispectral image (HrMSI) and a low-resolution hyperspectral image (LrHSI). Traditional HMF methods often suffer from over-reliance on degradation priors. While current blind HMF methods reduce this dependence by estimating the spectral degradation process, they still struggle with common real-world challenges, particularly spatial misregistration and spectral range non-overlap between the observed pair. To address these limitations, we propose a novel blind HMF framework that explicitly tackles both issues. Specifically, we introduce a dedicated image registration strategy before the fusion stage, which explicitly strengthens the spatial mapping relationship between the images and thus eliminates the need for strict initial spatial alignment assumptions. Building upon this, we establish an optimization model for spectral response function (SRF) estimation. The proposed method then employs a decoupled reconstruction mechanism that partitions the HMF process based on spectral band overlap, followed by a spectral recovery module (SRM) to further refine the fused HrHSI, ensuring high spectral accuracy across the entire spectrum. Experimental results demonstrate the exceptional performance of the proposed method in challenging HMF scenarios with significant spatial misregistration. Compared to existing approaches, the proposed method exhibits conclusive superior robustness to spatial misregistration in both SRF estimation and image fusion, achieving higher spectral accuracy in non-overlapping bands and delivering better spatial-spectral reconstruction. Notably, the SRM in the proposed method is highly portable and can be integrated as a general optimization component into other HMF methods to further enhance their performance.